Therefore, the purpose of this study is to conduct a classification of available VFR technologies from the perspective of consumer experience, particularly cognitive vs. Moreover, a variety of VFR technologies exist in the marketplace with each using different solutions and with different capabilities, making it harder for retailers to decide on which one to adopt. However, while VFRs have technically been available for a while, they are less utilised because of consumers’ potential concerns of accuracy of the simulation. Virtual fitting rooms (VFR) bring great opportunities to the fashion industry by enabling consumers to virtually try on products. We discuss potential social, biological, and cognitive explanations for our results and contribute with design implications for games and future avatar customization systems. Based on the data collected during the avatar creation process, we can predict the participants' gender with an accuracy up to 91%, which open up new use cases for video games and avatar creation systems. However, women with higher age decrease the femininity and increase the masculinity of stereotypical faces. We also found that young persons design more androgynous avatars, while adults further increase the masculinity and femininity of their avatars. Our results show that with increasing age, men and women design increasingly realistic and less stylized avatars. In this paper, we investigate the effects of gender and age on the facial characteristics of 4,215 virtual faces created by 1,475 participants (994 male, 481 female) mainly from Central Europe using a web-based avatar creation system and the Caucasian average face. However, it is currently unknown how the preferred characteristics of avatar faces depend on the players' age and gender or if these demographics can be predicted based on the data provided by an avatar creation system. Previous work investigates how users create their own avatars and determines the generally preferred characteristics of virtual faces. The characteristics of virtual faces can be important factors for avatars and characters in video games. We show that our method significantly outperforms a mean baseline predictor and report on a human study that shows that we can decode photofits that are visually plausible and close to the observer's mental image. We train the neural scoring network on a novel dataset containing gaze data of 19 participants looking at collages of synthetic faces. Finally, image features are aggregated into a single feature vector as a linear combination of all features weighted by relevance which a decoder decodes into the final photofit. A neural scoring network compares the human and neural attention and predicts a relevance score for each extracted image feature. The encoder extracts image features and predicts a neural activation map for each face looked at by a human observer. Our method combines three neural networks: An encoder, a scoring network, and a decoder. We propose a novel method that leverages human fixations to visually decode the image a person has in mind into a photofit (facial composite). The application has been validated through a cluster analysis of procedurally generated faces from 569 participants which created 1730 faces. We integrate the face creation system into a web application, which allows us to conduct studies in the large. We developed a 3D model of the Caucasian average face and implemented design parameters that can be manipulated to change the face appearance. Therefore, we developed a system that enables researchers to study the design process of virtual faces as well as the perception of such faces. Studying the design of virtual characters is challenging as it requires to have tools at hand that enables the creation of virtual characters. Virtual characters are widely used in games, virtual therapies, movie productions, and as avatars in e-commerce or in e-education. In video games, this is especially important to improve the design of virtual characters and to understand their creation process. The effect of facial features on human’s perception and emotion is widely studied in different disciplines.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |